A clustering algorithm for mixed numeric and categorical data
type: Item
instance of: scholarly article
title: A clustering algorithm for mixed numeric and categorical data
zbMATH Open document ID: 1058.93025
author: Ohn Mar San
author: Van-Nam Huynh
author: Yoshiteru Nakamori
published in: Journal of Systems Science and Complexity
publication date: 2004-06-18T00:00:00Z
review text: The paper displays a clustering algorithm to solve a data partition problem involving both numeric and categorical data. The main contribution is to develop a (new?) method to dynamically update the \(k\) cluster centers in order to maximize the intra cluster similarity of objects. The similarity distance involves both numerical and categorical attributes. The distance of categorical values is nothing but the so-called Hamming distance.
Mathematics Subject Classification ID: 93B40
Mathematics Subject Classification ID: 62D05
zbMATH DE Number: 2076084
zbMATH Keywords: clustering algorithm
zbMATH Keywords: data partition problem
zbMATH Keywords: categorical data
zbMATH Keywords: Hamming distance
reviewed by: Guy Jumarie
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 2076084